Aggregating Asynchronous Trust Outcomes for Mobile Security
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Solution Overview
Problem
Mobile computing devices face frequent authentication interruptions due to inconsistent security decisions from various components, leading to user inconvenience and potential security vulnerabilities when security preferences are set to minimize interruptions.
Innovation Solution
The system aggregates trust levels from sensors using heuristics, mathematical optimization, decision trees, machine learning, or artificial intelligence, determining an aggregated trust outcome to enable, disable, or relax security measures based on user confidence, allowing for intelligent authentication prompt management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication prompts are frequently presented to ensure security, then security reliability is improved, but user convenience deteriorates due to frequent interruptions
Solution Approach 1:
The system dynamically adjusts authentication requirements based on real-time sensor data and contextual information. Trust levels are continuously updated as sensors detect changes in environment, device state, or user behavior patterns, allowing the system to transition between secure and convenient modes automatically without user intervention
Solution Approach 2:
The system changes the parameter of authentication strictness based on aggregated sensor signals and trust outcomes. When trust level is high, authentication prompts are relaxed or eliminated; when trust level decreases, authentication requirements are strengthened. This parameter adjustment resolves the contradiction by making security adaptive rather than static
2Ease of operation
If security preferences are set to minimize authentication interruptions, then user convenience is improved, but security reliability deteriorates as the device becomes vulnerable to unauthorized access
Solution Approach 1:
The system continuously monitors sensor data, trust levels, and authentication outcomes to provide feedback that adjusts security behavior. When unauthorized access attempts are detected or trust levels drop, the system automatically increases authentication strictness, creating a feedback loop that maintains security while minimizing unnecessary interruptions for authorized users
Solution Approach 2:
The system autonomously manages security adjustments without requiring user configuration or intervention. It self-adjusts authentication requirements based on sensor aggregation and trust outcome analysis, eliminating the need for users to manually balance convenience and security preferences
3Adaptability or versatility
If multiple components make separate security decisions, then system adaptability is improved, but decision consistency deteriorates leading to conflicting authentication prompts
Solution Approach 1:
The system merges security decisions from multiple components into a unified authentication outcome. Instead of allowing separate components to make independent decisions that may conflict, the system aggregates their inputs and produces a single consistent authentication determination, eliminating conflicting prompts while preserving the adaptability of individual components
Data Source
AI summary
Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.